

import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCAmelCase				:    List[Any]        =		logging.getLogger(__name__)



@dataclass
class     __lowercase							:




			"""simple docstring"""







			_UpperCAmelCase  :				Dict     				=	42
			_UpperCAmelCase  :				Optional[Any]     				=	42
			_UpperCAmelCase  :				str     				=	42







@dataclass
class     __lowercase							:




			"""simple docstring"""







			_UpperCAmelCase  :				Optional[int]     				=	42
			_UpperCAmelCase  :				Optional[Any]     				=	42
			_UpperCAmelCase  :				Union[str, Any]     				=	None
			_UpperCAmelCase  :				Union[str, Any]     				=	None







class     __lowercase							(					__lowercase      ):




			"""simple docstring"""







			_UpperCAmelCase  :				Union[str, Any]     				=	'''train'''
			_UpperCAmelCase  :				Union[str, Any]     				=	'''dev'''
			_UpperCAmelCase  :				Optional[int]     				=	'''test'''







class     __lowercase							:




			"""simple docstring"""







			@staticmethod
			def 			_SCREAMING_SNAKE_CASE     (					lowerCAmelCase__    :	Optional[int]  ,		lowerCAmelCase__    :	Union[Split, str]):
									raise NotImplementedError



			@staticmethod
			def 			_SCREAMING_SNAKE_CASE     (					lowerCAmelCase__    :	str):
									raise NotImplementedError




			@staticmethod
			def 			_SCREAMING_SNAKE_CASE     (					lowerCAmelCase__    :	List[InputExample]  ,		lowerCAmelCase__    :	List[str]  ,		lowerCAmelCase__    :	int  ,		lowerCAmelCase__    :	PreTrainedTokenizer  ,		lowerCAmelCase__    :	Optional[int]=False  ,		lowerCAmelCase__    :	List[Any]="[CLS]"  ,		lowerCAmelCase__    :	Optional[int]=1  ,		lowerCAmelCase__    :	Union[str, Any]="[SEP]"  ,		lowerCAmelCase__    :	Tuple=False  ,		lowerCAmelCase__    :	Dict=False  ,		lowerCAmelCase__    :	Optional[int]=0  ,		lowerCAmelCase__    :	Union[str, Any]=0  ,		lowerCAmelCase__    :	Tuple=-100  ,		lowerCAmelCase__    :	str=0  ,		lowerCAmelCase__    :	Any=True  ,		):
									SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			{label: i for i, label in enumerate(lowerCAmelCase__)}

									SCREAMING_SNAKE_CASE_:					Tuple    						=			[]
									for ex_index, example in enumerate(lowerCAmelCase__):
															if ex_index % 1_0000 == 0:
																					logger.info("Writing example %d of %d"  ,		lowerCAmelCase__  ,		len(lowerCAmelCase__))

															SCREAMING_SNAKE_CASE_:					List[str]    						=			[]
															SCREAMING_SNAKE_CASE_:					Dict    						=			[]
															for word, label in zip(example.words  ,		example.labels):
																					SCREAMING_SNAKE_CASE_:					int    						=			tokenizer.tokenize(lowerCAmelCase__)

																					# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																					if len(lowerCAmelCase__) > 0:
																											tokens.extend(lowerCAmelCase__)
																											# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																											label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowerCAmelCase__) - 1))

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
															SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			tokenizer.num_special_tokens_to_add()
															if len(lowerCAmelCase__) > max_seq_length - special_tokens_count:
																					SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			tokens[: (max_seq_length - special_tokens_count)]
																					SCREAMING_SNAKE_CASE_:					str    						=			label_ids[: (max_seq_length - special_tokens_count)]

															# The convention in BERT is:
															# (a) For sequence pairs:
															#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
															#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
															# (b) For single sequences:
															#  tokens:   [CLS] the dog is hairy . [SEP]
															#  type_ids:   0   0   0   0  0     0   0
															#
															# Where "type_ids" are used to indicate whether this is the first
															# sequence or the second sequence. The embedding vectors for `type=0` and
															# `type=1` were learned during pre-training and are added to the wordpiece
															# embedding vector (and position vector). This is not *strictly* necessary
															# since the [SEP] token unambiguously separates the sequences, but it makes
															# it easier for the model to learn the concept of sequences.
															#
															# For classification tasks, the first vector (corresponding to [CLS]) is
															# used as the "sentence vector". Note that this only makes sense because
															# the entire model is fine-tuned.
															tokens += [sep_token]
															label_ids += [pad_token_label_id]
															if sep_token_extra:
																					# roberta uses an extra separator b/w pairs of sentences
																					tokens += [sep_token]
																					label_ids += [pad_token_label_id]
															SCREAMING_SNAKE_CASE_:					Tuple    						=			[sequence_a_segment_id] * len(lowerCAmelCase__)

															if cls_token_at_end:
																					tokens += [cls_token]
																					label_ids += [pad_token_label_id]
																					segment_ids += [cls_token_segment_id]
															else:
																					SCREAMING_SNAKE_CASE_:					int    						=			[cls_token] + tokens
																					SCREAMING_SNAKE_CASE_:					Tuple    						=			[pad_token_label_id] + label_ids
																					SCREAMING_SNAKE_CASE_:					str    						=			[cls_token_segment_id] + segment_ids

															SCREAMING_SNAKE_CASE_:					int    						=			tokenizer.convert_tokens_to_ids(lowerCAmelCase__)

															# The mask has 1 for real tokens and 0 for padding tokens. Only real
															# tokens are attended to.
															SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			[1 if mask_padding_with_zero else 0] * len(lowerCAmelCase__)

															# Zero-pad up to the sequence length.
															SCREAMING_SNAKE_CASE_:					Optional[int]    						=			max_seq_length - len(lowerCAmelCase__)
															if pad_on_left:
																					SCREAMING_SNAKE_CASE_:					Optional[int]    						=			([pad_token] * padding_length) + input_ids
																					SCREAMING_SNAKE_CASE_:					str    						=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																					SCREAMING_SNAKE_CASE_:					Tuple    						=			([pad_token_segment_id] * padding_length) + segment_ids
																					SCREAMING_SNAKE_CASE_:					Any    						=			([pad_token_label_id] * padding_length) + label_ids
															else:
																					input_ids += [pad_token] * padding_length
																					input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																					segment_ids += [pad_token_segment_id] * padding_length
																					label_ids += [pad_token_label_id] * padding_length

															assert len(lowerCAmelCase__) == max_seq_length
															assert len(lowerCAmelCase__) == max_seq_length
															assert len(lowerCAmelCase__) == max_seq_length
															assert len(lowerCAmelCase__) == max_seq_length

															if ex_index < 5:
																					logger.info("*** Example ***")
																					logger.info("guid: %s"  ,		example.guid)
																					logger.info("tokens: %s"  ,		" ".join([str(lowerCAmelCase__) for x in tokens]))
																					logger.info("input_ids: %s"  ,		" ".join([str(lowerCAmelCase__) for x in input_ids]))
																					logger.info("input_mask: %s"  ,		" ".join([str(lowerCAmelCase__) for x in input_mask]))
																					logger.info("segment_ids: %s"  ,		" ".join([str(lowerCAmelCase__) for x in segment_ids]))
																					logger.info("label_ids: %s"  ,		" ".join([str(lowerCAmelCase__) for x in label_ids]))

															if "token_type_ids" not in tokenizer.model_input_names:
																					SCREAMING_SNAKE_CASE_:					Any    						=			None

															features.append(
															    InputFeatures(
															        input_ids=lowerCAmelCase__  ,		attention_mask=lowerCAmelCase__  ,		token_type_ids=lowerCAmelCase__  ,		label_ids=lowerCAmelCase__))
									return features


if is_torch_available():
						import torch
						from torch import nn
						from torch.utils.data import Dataset



						class     __lowercase							(					__lowercase      ):




									"""simple docstring"""







									_UpperCAmelCase  :				List[Any]     				=	42
									_UpperCAmelCase  :				Optional[Any]     				=	nn.CrossEntropyLoss().ignore_index



									def __init__(					self    :	Any  ,		lowerCAmelCase__    :	TokenClassificationTask  ,		lowerCAmelCase__    :	str  ,		lowerCAmelCase__    :	PreTrainedTokenizer  ,		lowerCAmelCase__    :	List[str]  ,		lowerCAmelCase__    :	str  ,		lowerCAmelCase__    :	Optional[int] = None  ,		lowerCAmelCase__    :	List[Any]=False  ,		lowerCAmelCase__    :	Split = Split.train  ,		):
															# Load data features from cache or dataset file
															SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			os.path.join(
															    lowerCAmelCase__  ,		"cached_{}_{}_{}".format(mode.value  ,		tokenizer.__class__.__name__  ,		str(lowerCAmelCase__))  ,		)

															# Make sure only the first process in distributed training processes the dataset,
															# and the others will use the cache.
															SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			cached_features_file + ".lock"
															with FileLock(lowerCAmelCase__):
																					if os.path.exists(lowerCAmelCase__) and not overwrite_cache:
																											logger.info(F"Loading features from cached file {cached_features_file}")
																											SCREAMING_SNAKE_CASE_:					Any    						=			torch.load(lowerCAmelCase__)
																					else:
																											logger.info(F"Creating features from dataset file at {data_dir}")
																											SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			token_classification_task.read_examples_from_file(lowerCAmelCase__  ,		lowerCAmelCase__)
																											# TODO clean up all this to leverage built-in features of tokenizers
																											SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			token_classification_task.convert_examples_to_features(
																											    lowerCAmelCase__  ,		lowerCAmelCase__  ,		lowerCAmelCase__  ,		lowerCAmelCase__  ,		cls_token_at_end=bool(model_type in ["xlnet"])  ,		cls_token=tokenizer.cls_token  ,		cls_token_segment_id=2 if model_type in ["xlnet"] else 0  ,		sep_token=tokenizer.sep_token  ,		sep_token_extra=lowerCAmelCase__  ,		pad_on_left=bool(tokenizer.padding_side == "left")  ,		pad_token=tokenizer.pad_token_id  ,		pad_token_segment_id=tokenizer.pad_token_type_id  ,		pad_token_label_id=self.pad_token_label_id  ,		)
																											logger.info(F"Saving features into cached file {cached_features_file}")
																											torch.save(self.features  ,		lowerCAmelCase__)



									def __len__(					self    :	Optional[Any]):
															return len(self.features)




									def __getitem__(					self    :	Dict  ,		lowerCAmelCase__    :	List[Any]):
															return self.features[i]


if is_tf_available():
						import tensorflow as tf



						class     __lowercase							:




									"""simple docstring"""







									_UpperCAmelCase  :				str     				=	42
									_UpperCAmelCase  :				int     				=	-100



									def __init__(					self    :	Optional[int]  ,		lowerCAmelCase__    :	TokenClassificationTask  ,		lowerCAmelCase__    :	str  ,		lowerCAmelCase__    :	PreTrainedTokenizer  ,		lowerCAmelCase__    :	List[str]  ,		lowerCAmelCase__    :	str  ,		lowerCAmelCase__    :	Optional[int] = None  ,		lowerCAmelCase__    :	int=False  ,		lowerCAmelCase__    :	Split = Split.train  ,		):
															SCREAMING_SNAKE_CASE_:					Tuple    						=			token_classification_task.read_examples_from_file(lowerCAmelCase__  ,		lowerCAmelCase__)
															# TODO clean up all this to leverage built-in features of tokenizers
															SCREAMING_SNAKE_CASE_:					int    						=			token_classification_task.convert_examples_to_features(
															    lowerCAmelCase__  ,		lowerCAmelCase__  ,		lowerCAmelCase__  ,		lowerCAmelCase__  ,		cls_token_at_end=bool(model_type in ["xlnet"])  ,		cls_token=tokenizer.cls_token  ,		cls_token_segment_id=2 if model_type in ["xlnet"] else 0  ,		sep_token=tokenizer.sep_token  ,		sep_token_extra=lowerCAmelCase__  ,		pad_on_left=bool(tokenizer.padding_side == "left")  ,		pad_token=tokenizer.pad_token_id  ,		pad_token_segment_id=tokenizer.pad_token_type_id  ,		pad_token_label_id=self.pad_token_label_id  ,		)

															def gen():
																					for ex in self.features:
																											if ex.token_type_ids is None:
																																	yield (
																																	    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																																	    ex.label_ids,
																																	)
																											else:
																																	yield (
																																	    {
																																	        "input_ids": ex.input_ids,
																																	        "attention_mask": ex.attention_mask,
																																	        "token_type_ids": ex.token_type_ids,
																																	    },
																																	    ex.label_ids,
																																	)

															if "token_type_ids" not in tokenizer.model_input_names:
																					SCREAMING_SNAKE_CASE_:					Tuple    						=			tf.data.Dataset.from_generator(
																					    lowerCAmelCase__  ,		({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)  ,		(
																					        {"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])},
																					        tf.TensorShape([None]),
																					    )  ,		)
															else:
																					SCREAMING_SNAKE_CASE_:					int    						=			tf.data.Dataset.from_generator(
																					    lowerCAmelCase__  ,		({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)  ,		(
																					        {
																					            "input_ids": tf.TensorShape([None]),
																					            "attention_mask": tf.TensorShape([None]),
																					            "token_type_ids": tf.TensorShape([None]),
																					        },
																					        tf.TensorShape([None]),
																					    )  ,		)



									def 			_SCREAMING_SNAKE_CASE     (					self    :	str):
															SCREAMING_SNAKE_CASE_:					Dict    						=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))

															return self.dataset



									def __len__(					self    :	Optional[Any]):
															return len(self.features)




									def __getitem__(					self    :	Dict  ,		lowerCAmelCase__    :	Any):
															return self.features[i]








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase    :   Optional[int]						=			{
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['MobileViTFeatureExtractor']
		lowercase    :   Tuple						=			['MobileViTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[Any]						=			[
		    'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MobileViTForImageClassification',
		    'MobileViTForSemanticSegmentation',
		    'MobileViTModel',
		    'MobileViTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[Any]						=			[
		    'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFMobileViTForImageClassification',
		    'TFMobileViTForSemanticSegmentation',
		    'TFMobileViTModel',
		    'TFMobileViTPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilevit import MobileViTFeatureExtractor
				from .image_processing_mobilevit import MobileViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevit import (
				    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTForImageClassification,
				    MobileViTForSemanticSegmentation,
				    MobileViTModel,
				    MobileViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilevit import (
				    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileViTForImageClassification,
				    TFMobileViTForSemanticSegmentation,
				    TFMobileViTModel,
				    TFMobileViTPreTrainedModel,
				)


else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)